Unsupervised representation learning seeks to derive useful representations of speech waveforms without any human annotations. The learned representations are reused for downstream tasks, such as predicting caller intent or dialog action.
As HarperValleyBank is a small dataset, it is a suitable candidate to measure the effectiveness of speech representations. In our experiments, we pretrain representations on the 100 hour split or 960 hour split of LibriSpeech [1]}.
